A comparative evaluation of state-of-the-art community detection algorithms for multiplex networks

Abstract: Community detection is the study of discovering groups of nodes more connected toeach other than to other nodes inside a network. These groups give insight into the inner workings of a network and their discovery has practical applications in many fields of science.A multiplex network is a special type of multilayer network where multiple graphs co-exist on different layers that are stacked on top of each other into one structure where each node in a single layer has representation on every other layer in themultiplex. These networks are sophisticated attempts to model real world systems and the research in this area has contributed to increased insight and knowledge too ther fields of science such as social network analysis, medicine, finance and physics. In this thesis, we will provide an overview of three different multiplex community detection algorithms and a fair evaluation in regards to their resource usage and accuracy in a unified computing environment. The algorithms are tested against synthetically generated datasets and real life networks.